Reconstructing the release history of a contaminant source with different precision via the ensemble smoother with multiple data assimilation

Chen, Zi; Xu, Teng; Gómez‐Hernández, J. Jaime; Zanini, Andrea; Zhou, Qin

doi:10.1016/j.jconhyd.2022.104115

Cited by 12 publications

(3 citation statements)

References 55 publications

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“…There are many applications of ES-MDA for the estimation of source information or model parameters (Bao et al, 2020;Chen, Xu, et al, 2023;Rafiee & Reynolds, 2017;Todaro et al, 2021Todaro et al, , 2022Xu et al, 2021Xu et al, , 2022. After referring to these studies, the ensemble size and the number of iterations were set to 300 and 5, respectively.…”

Section: Ensemble Smoother With Multiple Data Assimilationmentioning

confidence: 99%

A Comparison of Inversion Methods for Surrogate‐Based Groundwater Contamination Source Identification With Varying Degrees of Model Complexity

Chang,

Guo,

Chen

et al. 2024

Water Resources Research

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Accurate identification of groundwater contamination sources is important for designing efficacious site remediation strategies. Currently, the methods for identifying contamination sources mainly fall into three distinct categories: simulation optimization, Bayesian inference, and data assimilation. Each method has its own advantages and disadvantages under specific site conditions. To evaluate the applicability of these methods, we chose one representative inversion algorithm from each category, namely the Improved Butterfly Optimization Algorithm (IBOA) for simulation optimization, the Ensemble Smoother with Multiple Data Assimilation (ES‐MDA) for data assimilation, and the DiffeRential Evolution Adaptive Metropolis with a Snooker Update and Sampling from a Past Archive (DREAM(ZS)) for Bayesian inference. We conducted a comprehensive evaluation of these methods' performance under different model complexities, employing a surrogate model as a substitute for the complex forward model. By addressing two distinct problems involving conservative pollutant transport and Light Non‐Aqueous Phase Liquid (LNAPL) transport with biodegradation, we employed four criteria (elapsed time, result accuracy, posterior probability distribution, and noise resistance) for evaluation. The findings unequivocally indicate that DREAM(ZS) outperforms others in terms of result accuracy and posterior probability distribution. It also adeptly navigates the interrelations among disparate unknown variables. The strength of ES‐MDA lies in its efficiency. It achieves relatively satisfactory results with a reduced computational burden. In contrast, IBOA underperforms in both test problems. In terms of resistance to noise, both DREAM(ZS) and ES‐MDA perform better than IBOA does.

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Section: Ensemble Smoother With Multiple Data Assimilationmentioning

confidence: 99%

A Comparison of Inversion Methods for Surrogate‐Based Groundwater Contamination Source Identification With Varying Degrees of Model Complexity

Chang,

Guo,

Chen

et al. 2024

Water Resources Research

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“…The stable isotope methods can provide precise apportionment of sources, but only for the isotope-related contaminant. As for the model-based numerical inversion methods, they are believed to be more suitable for local scale research [21][22][23][24]. Meanwhile, multivariate statistical approaches have gained popularity among researchers due to their convenience and efficiency for regional scale problems [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Groundwater Quality Using APCS-MLR Model: A Case Study in the Pilot Promoter Region of Yangtze River Delta Integration Demonstration Zone, China

Chen

Zhou

et al. 2023

Water

Self Cite

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Groundwater contaminant source identification is an endeavor task in highly developed areas that have been impacted by diverse natural processes and anthropogenic activities. In this study, groundwater samples from 84 wells in the pilot promoter region of the Yangtze River Delta integration demonstration zone in eastern China were collected and then analyzed for 17 groundwater quality parameters. The principal component analysis (PCA) method was utilized to recognize the natural and anthropogenic aspects impacting the groundwater quality; furthermore, the absolute principal component score-multiple linear regression (APCS-MLR) model was employed to quantify the contribution of potential sources to each groundwater quality parameter. The results demonstrated that natural hydro-chemical evolution, agricultural activities, domestic sewage, textile industrial effluent and other industrial activities were responsible for the status of groundwater quality in the study area. Meanwhile, the contribution of these five sources obtained by the APCS-MLR model were ranked as natural hydro-chemical evolution (18.89%) > textile industrial effluent (18.18%) > non-point source pollution from agricultural activities (17.08%) > other industrial activities (15.09%) > domestic sewage (4.19%). It is believed that this contaminant source apportionment result could provide a reliable basis to the local authorities for groundwater pollution management.

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“…It contains useful information on contaminant fate and transport which can help water management, remediation, and assessment. For instance, a common application of BTTP is to recover contamination history and identify responsible parties, where the BTTP's peak captures the most likely release time of contaminants from the source (Skaggs and Kabala, 1994;Woodbury and Ulrych, 1996;Woodbury 35 et al, 1998;Sun et al, 2006aSun et al, , 2006bJha and Datta, 2015;Yeh et al, 2015;Jamshidi et al, 2020;Chen et al, 2023). BTTP can also be used to date groundwater since BTTP characterizes the age distribution of groundwater due to borehole mixture and/or hydrodynamic dispersion in regional-scale aquifers (Weissmann et al, 2002;Cornaton and Perrochet, 2006;LaBolle et al, 2006;Konikow, 2007a, 2007b;Janssen et al, 2008;McMahon et al, 2008;Maxwell et al, 2016;Ponprasit et al, 2022;Mao et al, 2023).…”

mentioning

confidence: 99%

Adjoint subordination to calculate backward travel time probability of pollutants in water with various velocity resolutions

Zhang

Fogg

Sun

et al. 2023

Preprint

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Abstract. Backward probabilities such as backward travel time probability density function for pollutants in natural aquifers/rivers had been used by hydrologists for decades in water-quality related applications. Reliable calculation of backward probabilities, however, has been challenged by non-Fickian pollutant transport dynamics and variability in the resolution of velocity at study sites. To address these two issues, we built an adjoint model by deriving a backward-in-time fractional-derivative transport equation subordinated to regional flow, developed a Lagrangian solver, and applied the model/solver to backtrack pollutant transport in various flow systems. The adjoint model applies subordination to a reversed regional flow field, converts forward-in-time boundaries to either absorbing or reflective boundaries, and reverses the tempered stable density to define backward mechanical dispersion. The corresponding Lagrangian solver is computationally efficient in projecting backward super-diffusive mechanical dispersion along streamlines. Field applications demonstrate that the adjoint subordination model can successfully recover release history, dated groundwater age, and spatial location(s) of pollutant source(s) for flow systems with either upscaled constant velocity, non-uniform divergent flow field, or fine-resolution velocities in a non-stationary, regional-scale aquifer, where non-Fickian transport significantly affects pollutant dynamics and backward probability characteristics. Caution is needed when identifying the phase-sensitive (aqueous versus absorbed) pollutant source in natural media. Possible extensions of the adjoint subordination model are also discussed and tested for quantifying backward probabilities of pollutants in more complex media, such as discrete fracture networks.

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Reconstructing the release history of a contaminant source with different precision via the ensemble smoother with multiple data assimilation

Cited by 12 publications

References 55 publications

A Comparison of Inversion Methods for Surrogate‐Based Groundwater Contamination Source Identification With Varying Degrees of Model Complexity

A Comparison of Inversion Methods for Surrogate‐Based Groundwater Contamination Source Identification With Varying Degrees of Model Complexity

Assessment of Groundwater Quality Using APCS-MLR Model: A Case Study in the Pilot Promoter Region of Yangtze River Delta Integration Demonstration Zone, China

Adjoint subordination to calculate backward travel time probability of pollutants in water with various velocity resolutions

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